Techniques for obtaining intravascular ultrasound (IVUS) images of an interior vessel in the body, such as a coronary artery, are known in the art. Generally, a catheter including an ultrasound apparatus is introduced into the vessel. As the catheter is gradually moved along the vessel, the ultrasound apparatus transmits ultrasonic signals and detects the reflected signals. A processing device derives an image based on the detected ultrasound signals. In this manner, a sequence of images of the interior structure of the vessel is obtained. However the image sequence by itself does not reveal or identify the exact position of different features of the vessel. For example, with such images of a coronary artery, it is difficult to distinguish between areas of blood and tissue, and regions of calcium deposits. This information can assist and enhance the performance of medical operations, including various types of diagnostic, therapeutic, and preventative procedures.
U.S. Pat. No. 5,771,895 to Slager entitled “Catheter for obtaining three-dimensional reconstruction of a vascular lumen and wall”, is directed to a catheter system and method for obtaining three-dimensional reconstruction of a vessel by X-ray angiography and intravascular ultrasound. A catheter is pulled back through a blood vessel at a certain speed. The catheter system includes an elongated sheath having proximal and distal regions. The distal region houses an ultrasound transducer and permits rotation and longitudinal translation. The transducer captures a stack of cross-sectional ultrasound images during pull-back. A sequence of radiopaque markers is disposed along the sheath, equally spaced. The markers speed up the three-dimensional reconstruction of the catheter centerline, and may also provide positional feedback during pull-back.
A computer program detects the contours of the luminal and wall-adventitia boundaries at fixed intervals, based on end diastolic samples of the IVUS images. Biplane fluoroscopy records the start and end of the pull-back, and biplane angiography is performed without changes in geometrical X-ray settings. The geometry of the longitudinal centerline, representing the path of the IVUS transducer, is determined using the biplane fluoroscopy image data. A three-dimensional reconstruction of the vessel is made using the IVUS data stack, the catheter path, and the lumen contours. In particular, the contours are combined with the centerline using features of the angiograms, such as the position of the centerline relative to the lumen border, to establish the rotational position of the contours around the spatial curve.
U.S. Pat. No. 6,152,878 to Nachtomy et al entitled “Intravascular ultrasound enhanced image and signal processing”, is directed to a device and method for processing intravascular ultrasound image information to remove distortions and inaccuracies caused by various types of motion in the catheter and the bodily lumen. A transducer attached to a catheter emits and receives ultrasonic signals. The catheter is inserted into a blood vessel. An ultrasound beam from the transducer is continuously rotated within the vessel, forming a 360° internal cross-sectional image in a transverse plane of the vessel. The catheter is gradually moved along the blood vessel, and images of various segments of the vessel are obtained.
The detected ultrasound signal is processed to form a set of vectors comprising digitized data. Each vector represents the ultrasonic response of a different angular sector of the vessel. The digitized vectors are initially stored in a matrix in polar coordinate form. The polar matrix is converted into a matrix in Cartesian coordinate form, in which the axes correspond to the Cartesian representation of the cross-section of the vessel. The image is then further processed and transferred to a display.
The images are stabilized in order to compensate for different types of relative motion experienced by the catheter and the vessel. These types of motion include: rotation in the plane of the image, Cartesian displacement, global vasomotion or a radial contraction and expansion of the entire vessel, local vasomotion or a radial contraction and expansion of different parts of the vessel with different magnitudes and directions, local motion by different tissue, and through plane motion or movements perpendicular to the plane of the image.
The first three types of motion are stabilized using global stabilization, which compare whole parts of the image to one another. The next two types of motions in the list are stabilized by applying closeness operations on a localized basis. The last type of motion is stabilized using cardiovascular periodicity detection.
In global stabilization, shift evaluation is performed using a closeness operation. A first image is transformed and its closeness to its predecessor second image is measured. The transformation is performed by shifting the entire first image along a combination of axes. The images are then compared using a predefined function. The transformation is repeated until all shifts are measured and the global extremum of the comparisons indicates the direction and magnitude of the movement between the two images.
“A State-Space Model for a Sequence of Image Characterisitics” by Dethlefsen, Hansen, and Lundbye-Christensen, discusses an automated method for determining the evolution of the cross-sectional area of a coronary artery. A sequence of images of the coronary artery is obtained through ultrasound imaging. The artery wall is modeled as a pulsating disc parameterized by a center and a radius. The center and radius may both exhibit fluctuations due to random factors. The cross-sectional area of the artery can be calculated at a given time from estimates of the center and the radius. The vector of image characteristics is estimated at any given time by utilizing the series of images previously observed and calculating the posterior mean and variance matrices. In order to obtain the series of posterior means, the recursive structure of the Kalman filter is combined with a Markov chain Monte Carlo method, such as the Metropolis-Hasting's algorithm.
“Near-infrared Raman Spectroscopy for In Vitro Human Coronary Artery Tissue Identification” by Silveira Jr, Zângaro, Pacheco, Sathaiah, Chavantes, and Pasqualucci, discusses the use of Near-Infrared Raman Spectroscopy for in vitro diagnosis of atheromatous plaque. An algorithm is presented that classifies the human coronary artery segments into two segments: non-pathologic (NP) or atherosclerotic (AT) plaque, based on spectral features extracted from Raman data. The classification is done using Mahalanobis distance using histopathological results as a gold standard.
A collection of coronary artery fragments are extracted and prepared. The samples are placed before the spectrograph of the NIRS and spectral data is obtained. The fragments are classified in four main tissue types by a pathologist. The spectra are separated according to histopathology, plotted, and spectral features obtained. The atheromatous plaque exhibited distinct Raman features, such as main bands at specific wavelengths, and a higher relative intensity. Different features of the spectra are used in classifying the spectra into two categories. For a clear separation into groups, separation surfaces are drawn based on the Mahalanobis distances, which takes into account the relative distance between the sample to the mean of a group as well as the covariance matrix of the data.